82
1600:Chemistry(比率: 24 %)
| DOI | タイトル | 著者 | ジャーナル | 発行年 | 科研費成果論文 |
|---|---|---|---|---|---|
| 10.1002/adma.201902765 | Machine Learning Interatomic Potentials As Emerging Tools For Materials Science | Deringer, Volker L., 0000-0001-6873-0278; Caro, Miguel A.; Csanyi, Gabor | Advanced Materials | 2019 | NA |
| 10.1002/advs.201801367 | Deep Learning Spectroscopy: Neural Networks For Molecular Excitation Spectra | Ghosh, Kunal; Stuke, Annika; Todorovi<U+0107>, Milica; Jorgensen, Peter Bjorn; Schmidt, Mikkel N.; Vehtari, Aki; Rinke, Patrick, 0000-0003-1898-723X | Advanced Science | 2019 | NA |
| 10.1016/j.automatica.2018.03.046 | Linear Predictors For Nonlinear Dynamical Systems: Koopman Operator Meets Model Predictive Control | Korda, Milan; Mezi<U+0107>, Igor | Automatica | 2018 | NA |
| 10.1016/j.cell.2020.01.021 | A Deep Learning Approach To Antibiotic Discovery | Stokes, Jonathan M.; Yang, Kevin; Swanson, Kyle; Jin, Wengong; Cubillos-Ruiz, Andres; Donghia, Nina M.; Macnair, Craig R.; French, Shawn; Carfrae, Lindsey A.; Bloom-Ackermann, Zohar; Tran, Victoria M.; Chiappino-Pepe, Anush; Badran, Ahmed H.; Andrews, Ian W.; Chory, Emma J.; Church, George M.; Brown, Eric D.; Jaakkola, Tommi S.; Barzilay, Regina; Collins, James J. | Cell | 2020 | NA |
| 10.1016/j.chempr.2020.02.017 | A Structure-Based Platform For Predicting Chemical Reactivity | Sandfort, Frederik; Strieth-Kalthoff, Felix; Kuhnemund, Marius; Beecks, Christian; Glorius, Frank | Chem | 2020 | NA |
| 10.1016/j.commatsci.2018.09.031 | Accelerating High-Throughput Searches For New Alloys With Active Learning Of Interatomic Potentials | Gubaev, Konstantin; Podryabinkin, Evgeny V.; Hart, Gus L.W.; Shapeev, Alexander V. | Computational Materials Science | 2019 | NA |
| 10.1016/j.cpc.2018.03.016 | Deepmd-Kit: A Deep Learning Package For Many-Body Potential Energy Representation And Molecular Dynamics | Wang, Han; Zhang, Linfeng, 0000-0002-8470-5846; Han, Jiequn; E, Weinan | Computer Physics Communications | 2018 | NA |
| 10.1016/j.cpc.2019.02.007 | Sgdml: Constructing Accurate And Data Efficient Molecular Force Fields Using Machine Learning | Chmiela, Stefan; Sauceda, Huziel E., 0000-0001-6091-3408; Poltavsky, Igor; Muller, Klaus-Robert; Tkatchenko, Alexandre | Computer Physics Communications | 2019 | NA |
| 10.1016/j.jcp.2017.11.039 | Hidden Physics Models: Machine Learning Of Nonlinear Partial Differential Equations | Raissi, Maziar; Karniadakis, George Em | Journal Of Computational Physics | 2018 | NA |
| 10.1021/acs.chemrev.0c01111 | Machine Learning Force Fields | Unke, Oliver T., 0000-0001-7503-406X; Chmiela, Stefan; Sauceda, Huziel E., 0000-0001-6091-3408; Gastegger, Michael; Poltavsky, Igor, 0000-0002-3188-7017; Schutt, Kristof T., 0000-0001-8342-0964; Tkatchenko, Alexandre, 0000-0002-1012-4854; Muller, Klaus-Robert, 0000-0002-3861-7685 | Chemical Reviews | 2021 | NA |
| 10.1021/acs.chemrev.8b00588 | Computational Ligand Descriptors For Catalyst Design | Durand, Derek J., 0000-0002-2956-6134; Fey, Natalie, 0000-0003-0609-475X | Chemical Reviews | 2019 | NA |
| 10.1021/acs.chemrev.9b00073 | Design And Optimization Of Catalysts Based On Mechanistic Insights Derived From Quantum Chemical Reaction Modeling | Ahn, Seihwan; Hong, Mannkyu, 0000-0002-7770-1230; Sundararajan, Mahesh; Ess, Daniel H., 0000-0001-5689-9762; Baik, Mu-Hyun, 0000-0002-8832-8187 | Chemical Reviews | 2019 | NA |
| 10.1021/acs.chemrev.9b00425 | Quantitative Structure<U+2013>Selectivity Relationships In Enantioselective Catalysis: Past, Present, And Future | Zahrt, Andrew F.; Athavale, Soumitra V.; Denmark, Scott E., 0000-0002-1099-9765 | Chemical Reviews | 2019 | NA |
| 10.1021/acs.jcim.9b00237 | Analyzing Learned Molecular Representations For Property Prediction | Yang, Kevin; Swanson, Kyle, 0000-0002-7385-7844; Jin, Wengong; Coley, Connor, 0000-0002-8271-8723; Eiden, Philipp; Gao, Hua; Guzman-Perez, Angel; Hopper, Timothy; Kelley, Brian; Mathea, Miriam; Palmer, Andrew; Settels, Volker; Jaakkola, Tommi; Jensen, Klavs, 0000-0001-7192-580X; Barzilay, Regina | Journal Of Chemical Information And Modeling | 2019 | NA |
| 10.1021/acs.jctc.8b00636 | Transferability In Machine Learning For Electronic Structure Via The Molecular Orbital Basis | Welborn, Matthew; Cheng, Lixue; Miller, Thomas F., 0000-0002-1882-5380 | Journal Of Chemical Theory And Computation | 2018 | NA |
| 10.1021/acs.jctc.8b00770 | Library-Based Lammps Implementation Of High-Dimensional Neural Network Potentials | Singraber, Andreas, 0000-0002-4330-1394; Behler, Jorg, 0000-0002-1220-1542; Dellago, Christoph, 0000-0001-9166-6235 | Journal Of Chemical Theory And Computation | 2019 | NA |
| 10.1021/acs.jctc.8b00908 | Schnetpack: A Deep Learning Toolbox For Atomistic Systems | Schutt, K. T., 0000-0001-8342-0964; Kessel, P.; Gastegger, M.; Nicoli, K. A.; Tkatchenko, A., 0000-0002-1012-4854; Muller, K.-R. | Journal Of Chemical Theory And Computation | 2018 | NA |
| 10.1021/acs.jctc.8b00959 | Fast And Accurate Uncertainty Estimation In Chemical Machine Learning | Musil, Felix; Willatt, Michael J.; Langovoy, Mikhail A.; Ceriotti, Michele, 0000-0003-2571-2832 | Journal Of Chemical Theory And Computation | 2019 | NA |
| 10.1021/acs.jctc.8b01092 | Parallel Multistream Training Of High-Dimensional Neural Network Potentials | Singraber, Andreas, 0000-0002-4330-1394; Morawietz, Tobias, 0000-0002-9385-8721; Behler, Jorg, 0000-0002-1220-1542; Dellago, Christoph, 0000-0001-9166-6235 | Journal Of Chemical Theory And Computation | 2019 | NA |
| 10.1021/acs.jctc.9b00181 | Physnet: A Neural Network For Predicting Energies, Forces, Dipole Moments, And Partial Charges | Unke, Oliver T., 0000-0001-7503-406X; Meuwly, Markus, 0000-0001-7930-8806 | Journal Of Chemical Theory And Computation | 2019 | NA |
| 10.1021/acs.jmedchem.9b00959 | Pushing The Boundaries Of Molecular Representation For Drug Discovery With The Graph Attention Mechanism | Xiong, Zhaoping; Wang, Dingyan; Liu, Xiaohong; Zhong, Feisheng; Wan, Xiaozhe; Li, Xutong; Li, Zhaojun; Luo, Xiaomin, 0000-0003-0426-3417; Chen, Kaixian; Jiang, Hualiang; Zheng, Mingyue, 0000-0002-3323-3092 | Journal Of Medicinal Chemistry | 2019 | NA |
| 10.1021/acs.jpca.9b08723 | Performance And Cost Assessment Of Machine Learning Interatomic Potentials | Zuo, Yunxing, 0000-0002-2734-7720; Chen, Chi, 0000-0001-8008-7043; Li, Xiangguo; Deng, Zhi; Chen, Yiming, 0000-0002-1501-5550; Behler, Jorg, 0000-0002-1220-1542; Csanyi, Gabor; Shapeev, Alexander V.; Thompson, Aidan P.; Wood, Mitchell A.; Ong, Shyue Ping, 0000-0001-5726-2587 | The Journal Of Physical Chemistry A | 2020 | NA |
| 10.1021/acs.jpclett.8b03026 | Deep Learning For Nonadiabatic Excited-State Dynamics | Chen, Wen-Kai; Liu, Xiang-Yang; Fang, Wei-Hai, 0000-0002-1668-465X; Dral, Pavlo O., 0000-0002-2975-9876; Cui, Ganglong, 0000-0002-9752-1659 | The Journal Of Physical Chemistry Letters | 2018 | NA |
| 10.1021/acs.jpclett.9b00085 | Bridging The Gap Between Direct Dynamics And Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks | Zhang, Yaolong; Zhou, Xueyao; Jiang, Bin, 0000-0003-2696-5436 | The Journal Of Physical Chemistry Letters | 2019 | NA |
| 10.1021/acs.jpclett.9b02037 | Embedded Atom Neural Network Potentials: Efficient And Accurate Machine Learning With A Physically Inspired Representation | Zhang, Yaolong; Hu, Ce; Jiang, Bin, 0000-0003-2696-5436 | The Journal Of Physical Chemistry Letters | 2019 | NA |
| 10.1021/acs.jpclett.9b03664 | Quantum Chemistry In The Age Of Machine Learning | Dral, Pavlo O., 0000-0002-2975-9876 | The Journal Of Physical Chemistry Letters | 2020 | NA |
| 10.1021/acscatal.9b01537 | Scope And Challenge Of Computational Methods For Studying Mechanism And Reactivity In Homogeneous Catalysis | Harvey, Jeremy N., 0000-0002-1728-1596; Himo, Fahmi, 0000-0002-1012-5611; Maseras, Feliu, 0000-0001-8806-2019; Perrin, Lionel, 0000-0002-0702-8749 | Acs Catalysis | 2019 | NA |
| 10.1021/acscatal.9b04952 | Automated In Silico Design Of Homogeneous Catalysts | Foscato, Marco, 0000-0001-7762-6931; Jensen, Vidar R., 0000-0003-2444-3220 | Acs Catalysis | 2020 | NA |
| 10.1021/acscentsci.8b00357 | Using Machine Learning To Predict Suitable Conditions For Organic Reactions | Gao, Hanyu, 0000-0002-6346-0739; Struble, Thomas J., 0000-0003-1695-2367; Coley, Connor W., 0000-0002-8271-8723; Wang, Yuran; Green, William H., 0000-0003-2603-9694; Jensen, Klavs F., 0000-0001-7192-580X | Acs Central Science | 2018 | NA |
| 10.1021/acscentsci.8b00551 | Transferable Machine-Learning Model Of The Electron Density | Grisafi, Andrea; Fabrizio, Alberto, 0000-0002-4440-3149; Meyer, Benjamin; Wilkins, David M.; Corminboeuf, Clemence, 0000-0001-7993-2879; Ceriotti, Michele, 0000-0003-2571-2832 | Acs Central Science | 2018 | NA |
| 10.1021/acscentsci.8b00913 | Machine Learning Of Coarse-Grained Molecular Dynamics Force Fields | Wang, Jiang; Olsson, Simon, 0000-0002-3927-7897; Wehmeyer, Christoph; Perez, Adria; Charron, Nicholas E.; De Fabritiis, Gianni, 0000-0003-3913-4877; Noe, Frank; Clementi, Cecilia, 0000-0001-9221-2358 | Acs Central Science | 2019 | NA |
| 10.1021/acscentsci.9b00576 | Molecular Transformer: A Model For Uncertainty-Calibrated Chemical Reaction Prediction | Schwaller, Philippe, 0000-0003-3046-6576; Laino, Teodoro; Gaudin, Theophile; Bolgar, Peter; Hunter, Christopher A.; Bekas, Costas; Lee, Alpha A., 0000-0002-9616-3108 | Acs Central Science | 2019 | NA |
| 10.1038/s41467-018-06169-2 | Towards Exact Molecular Dynamics Simulations With Machine-Learned Force Fields | Chmiela, Stefan; Sauceda, Huziel E., 0000-0001-6091-3408; Muller, Klaus-Robert; Tkatchenko, Alexandre | Nature Communications | 2018 | NA |
| 10.1038/s41467-018-07210-0 | Deep Learning For Universal Linear Embeddings Of Nonlinear Dynamics | Lusch, Bethany, 0000-0002-9521-9990; Kutz, J. Nathan; Brunton, Steven L. | Nature Communications | 2018 | NA |
| 10.1038/s41467-018-07520-3 | Constructing Exact Representations Of Quantum Many-Body Systems With Deep Neural Networks | Carleo, Giuseppe; Nomura, Yusuke; Imada, Masatoshi | Nature Communications | 2018 | TRUE |
| 10.1038/s41467-019-10827-4 | Approaching Coupled Cluster Accuracy With A General-Purpose Neural Network Potential Through Transfer Learning | Smith, Justin S.; Nebgen, Benjamin T., 0000-0001-5310-3263; Zubatyuk, Roman; Lubbers, Nicholas; Devereux, Christian; Barros, Kipton; Tretiak, Sergei, 0000-0001-5547-3647; Isayev, Olexandr, 0000-0001-7581-8497; Roitberg, Adrian E. | Nature Communications | 2019 | NA |
| 10.1038/s41467-019-12875-2 | Unifying Machine Learning And Quantum Chemistry With A Deep Neural Network For Molecular Wavefunctions | Schutt, K. T., 0000-0001-8342-0964; Gastegger, M.; Tkatchenko, A.; Muller, K.-R.; Maurer, R. J. | Nature Communications | 2019 | NA |
| 10.1038/s41524-019-0162-7 | Solving The Electronic Structure Problem With Machine Learning | Chandrasekaran, Anand, 0000-0002-2794-3717; Kamal, Deepak; Batra, Rohit; Kim, Chiho; Chen, Lihua; Ramprasad, Rampi, 0000-0003-4630-1565 | Npj Computational Materials | 2019 | NA |
| 10.1038/s41524-019-0236-6 | De Novo Exploration And Self-Guided Learning Of Potential-Energy Surfaces | Bernstein, Noam; Csanyi, Gabor; Deringer, Volker L., 0000-0001-6873-0278 | Npj Computational Materials | 2019 | NA |
| 10.1038/s41524-019-0261-5 | Coarse-Graining Auto-Encoders For Molecular Dynamics | Wang, Wujie; Gomez-Bombarelli, Rafael, 0000-0002-9495-8599 | Npj Computational Materials | 2019 | NA |
| 10.1038/s41524-020-0283-z | On-The-Fly Active Learning Of Interpretable Bayesian Force Fields For Atomistic Rare Events | Vandermause, Jonathan, 0000-0001-5559-3147; Torrisi, Steven B.; Batzner, Simon; Xie, Yu; Sun, Lixin; Kolpak, Alexie M.; Kozinsky, Boris, 0000-0002-0638-539X | Npj Computational Materials | 2020 | NA |
| 10.1038/s41534-020-0248-6 | Experimental Neural Network Enhanced Quantum Tomography | Palmieri, Adriano Macarone; Kovlakov, Egor; Bianchi, Federico; Yudin, Dmitry; Straupe, Stanislav, 0000-0001-9810-1958; Biamonte, Jacob D., 0000-0002-0590-3327; Kulik, Sergei | Npj Quantum Information | 2020 | NA |
| 10.1038/s41557-020-0544-y | Deep-Neural-Network Solution Of The Electronic Schrodinger Equation | Hermann, Jan, 0000-0002-2779-0749; Schatzle, Zeno, 0000-0002-5345-6592; Noe, Frank, 0000-0003-4169-9324 | Nature Chemistry | 2020 | NA |
| 10.1038/s41570-020-0189-9 | Exploring Chemical Compound Space With Quantum-Based Machine Learning | Von Lilienfeld, O. Anatole; Muller, Klaus-Robert; Tkatchenko, Alexandre, 0000-0002-1012-4854 | Nature Reviews Chemistry | 2020 | NA |
| 10.1038/s41578-019-0101-8 | Structure Prediction Drives Materials Discovery | Oganov, Artem R., 0000-0001-7082-9728; Pickard, Chris J., 0000-0002-9684-5432; Zhu, Qiang; Needs, Richard J. | Nature Reviews Materials | 2019 | NA |
| 10.1038/s41586-019-1384-z | Holistic Prediction Of Enantioselectivity In Asymmetric Catalysis | Reid, Jolene P.; Sigman, Matthew S. | Nature | 2019 | NA |
| 10.1038/s42256-019-0028-1 | Reconstructing Quantum States With Generative Models | Carrasquilla, Juan, 0000-0001-7263-3462; Torlai, Giacomo; Melko, Roger G.; Aolita, Leandro | Nature Machine Intelligence | 2019 | NA |
| 10.1039/c8sc04228d | A Graph-Convolutional Neural Network Model For The Prediction Of Chemical Reactivity | Coley, Connor W., 0000-0002-8271-8723; Jin, Wengong; Rogers, Luke; Jamison, Timothy F., 0000-0002-8601-7799; Jaakkola, Tommi S.; Green, William H., 0000-0003-2603-9694; Barzilay, Regina; Jensen, Klavs F., 0000-0001-7192-580X | Chemical Science | 2019 | NA |
| 10.1039/c9sc01742a | Machine Learning Enables Long Time Scale Molecular Photodynamics Simulations | Westermayr, Julia, 0000-0002-6531-0742; Gastegger, Michael, 0000-0001-7954-3275; Menger, Maximilian F. S. J.; Mai, Sebastian, 0000-0001-5327-8880; Gonzalez, Leticia, 0000-0001-5112-794X; Marquetand, Philipp, 0000-0002-8711-1533 | Chemical Science | 2019 | NA |
| 10.1039/c9sc02298h | A Quantitative Uncertainty Metric Controls Error In Neural Network-Driven Chemical Discovery | Janet, Jon Paul, 0000-0001-7825-4797; Duan, Chenru, 0000-0003-2592-4237; Yang, Tzuhsiung; Nandy, Aditya, 0000-0001-7137-5449; Kulik, Heather J., 0000-0001-9342-0191 | Chemical Science | 2019 | NA |
| 10.1039/c9sc05704h | Predicting Retrosynthetic Pathways Using Transformer-Based Models And A Hyper-Graph Exploration Strategy | Schwaller, Philippe, 0000-0003-3046-6576; Petraglia, Riccardo; Zullo, Valerio; Nair, Vishnu H.; Haeuselmann, Rico Andreas; Pisoni, Riccardo; Bekas, Costas; Iuliano, Anna, 0000-0002-6805-3366; Laino, Teodoro | Chemical Science | 2020 | NA |
| 10.1063/1.5011181 | Hierarchical Modeling Of Molecular Energies Using A Deep Neural Network | Lubbers, Nicholas, 0000-0002-9001-9973; Smith, Justin S., 0000-0001-7314-7896; Barros, Kipton, 0000-0002-1333-5972 | The Journal Of Chemical Physics | 2018 | NA |
| 10.1063/1.5011399 | Time-Lagged Autoencoders: Deep Learning Of Slow Collective Variables For Molecular Kinetics | Wehmeyer, Christoph, 0000-0002-9526-0328; Noe, Frank, 0000-0003-4169-9324 | The Journal Of Chemical Physics | 2018 | NA |
| 10.1063/1.5019779 | Schnet <U+2013> A Deep Learning Architecture For Molecules And Materials | Schutt, K. T., 0000-0001-8342-0964; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Muller, K.-R. | The Journal Of Chemical Physics | 2018 | NA |
| 10.1063/1.5020710 | Alchemical And Structural Distribution Based Representation For Universal Quantum Machine Learning | Faber, Felix A.; Christensen, Anders S., 0000-0002-7253-6897; Huang, Bing; Von Lilienfeld, O. Anatole | The Journal Of Chemical Physics | 2018 | NA |
| 10.1063/1.5023802 | Less Is More: Sampling Chemical Space With Active Learning | Smith, Justin S., 0000-0001-7314-7896; Nebgen, Ben; Lubbers, Nicholas, 0000-0002-9001-9973; Isayev, Olexandr, 0000-0001-7581-8497; Roitberg, Adrian E. | The Journal Of Chemical Physics | 2018 | NA |
| 10.1063/1.5025487 | Reweighted Autoencoded Variational Bayes For Enhanced Sampling (Rave) | Ribeiro, Joao Marcelo Lamim; Bravo, Pablo, 0000-0002-4784-4900; Wang, Yihang, 0000-0002-3566-2042; Tiwary, Pratyush | The Journal Of Chemical Physics | 2018 | NA |
| 10.1063/1.5053562 | Operators In Quantum Machine Learning: Response Properties In Chemical Space | Christensen, Anders S., 0000-0002-7253-6897; Faber, Felix A.; Von Lilienfeld, O. Anatole, 0000-0001-7419-0466 | The Journal Of Chemical Physics | 2019 | NA |
| 10.1063/1.5088393 | A Universal Density Matrix Functional From Molecular Orbital-Based Machine Learning: Transferability Across Organic Molecules | Cheng, Lixue, 0000-0002-7329-0585; Welborn, Matthew; Christensen, Anders S., 0000-0002-7253-6897; Miller, Thomas F., 0000-0002-1882-5380 | The Journal Of Chemical Physics | 2019 | NA |
| 10.1063/1.5090481 | Atom-Density Representations For Machine Learning | Willatt, Michael J., 0000-0002-2916-1233; Musil, Felix, 0000-0001-7401-012X; Ceriotti, Michele, 0000-0003-2571-2832 | The Journal Of Chemical Physics | 2019 | NA |
| 10.1063/1.5091842 | Unsupervised Machine Learning In Atomistic Simulations, Between Predictions And Understanding | Ceriotti, Michele, 0000-0003-2571-2832 | The Journal Of Chemical Physics | 2019 | NA |
| 10.1063/1.5126336 | Machine Learning For Interatomic Potential Models | Mueller, Tim, 0000-0001-8284-7747; Hernandez, Alberto, 0000-0001-9751-5171; Wang, Chuhong, 0000-0001-8993-3226 | The Journal Of Chemical Physics | 2020 | NA |
| 10.1063/1.5126701 | Fchl Revisited: Faster And More Accurate Quantum Machine Learning | Christensen, Anders S., 0000-0002-7253-6897; Bratholm, Lars A., 0000-0002-3565-5926; Faber, Felix A., 0000-0002-3576-4137; Anatole Von Lilienfeld, O., 0000-0001-7419-0466 | The Journal Of Chemical Physics | 2020 | NA |
| 10.1073/pnas.1906995116 | Data-Driven Discovery Of Coordinates And Governing Equations | Champion, Kathleen; Lusch, Bethany; Kutz, J. Nathan; Brunton, Steven L. | Proceedings Of The National Academy Of Sciences | 2019 | NA |
| 10.1103/physrevb.100.014105 | On-The-Fly Machine Learning Force Field Generation: Application To Melting Points | Jinnouchi, Ryosuke; Karsai, Ferenc; Kresse, Georg | Physical Review B | 2019 | NA |
| 10.1103/physrevb.99.014104 | Atomic Cluster Expansion For Accurate And Transferable Interatomic Potentials | Drautz, Ralf | Physical Review B | 2019 | NA |
| 10.1103/physrevb.99.064114 | Accelerating Crystal Structure Prediction By Machine-Learning Interatomic Potentials With Active Learning | Podryabinkin, Evgeny V.; Tikhonov, Evgeny V.; Shapeev, Alexander V.; Oganov, Artem R. | Physical Review B | 2019 | NA |
| 10.1103/physrevb.99.214306 | Constructing Neural Stationary States For Open Quantum Many-Body Systems | Yoshioka, Nobuyuki; Hamazaki, Ryusuke | Physical Review B | 2019 | TRUE |
| 10.1103/physrevlett.120.143001 | Deep Potential Molecular Dynamics: A Scalable Model With The Accuracy Of Quantum Mechanics | Zhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; E, Weinan | Physical Review Letters | 2018 | NA |
| 10.1103/physrevlett.121.167204 | Symmetries And Many-Body Excitations With Neural-Network Quantum States | Choo, Kenny; Carleo, Giuseppe; Regnault, Nicolas; Neupert, Titus | Physical Review Letters | 2018 | NA |
| 10.1103/physrevlett.122.065301 | Quantum Entanglement In Deep Learning Architectures | Levine, Yoav; Sharir, Or; Cohen, Nadav; Shashua, Amnon | Physical Review Letters | 2019 | NA |
| 10.1103/physrevlett.122.080602 | Solving Statistical Mechanics Using Variational Autoregressive Networks | Wu, Dian; Wang, Lei; Zhang, Pan | Physical Review Letters | 2019 | NA |
| 10.1103/physrevlett.122.225701 | Phase Transitions Of Hybrid Perovskites Simulated By Machine-Learning Force Fields Trained On The Fly With Bayesian Inference | Jinnouchi, Ryosuke; Lahnsteiner, Jonathan; Karsai, Ferenc; Kresse, Georg; Bokdam, Menno | Physical Review Letters | 2019 | NA |
| 10.1103/physrevlett.122.250501 | Variational Quantum Monte Carlo Method With A Neural-Network Ansatz For Open Quantum Systems | Nagy, Alexandra; Savona, Vincenzo | Physical Review Letters | 2019 | NA |
| 10.1103/physrevlett.122.250502 | Neural-Network Approach To Dissipative Quantum Many-Body Dynamics | Hartmann, Michael J., 0000-0002-8207-3806; Carleo, Giuseppe | Physical Review Letters | 2019 | NA |
| 10.1103/physrevlett.122.250503 | Variational Neural-Network Ansatz For Steady States In Open Quantum Systems | Vicentini, Filippo; Biella, Alberto; Regnault, Nicolas; Ciuti, Cristiano, 0000-0002-1134-7013 | Physical Review Letters | 2019 | NA |
| 10.1103/physrevmaterials.3.023804 | Active Learning Of Uniformly Accurate Interatomic Potentials For Materials Simulation | Zhang, Linfeng; Lin, De-Ye; Wang, Han; Car, Roberto; E, Weinan | Physical Review Materials | 2019 | NA |
| 10.1103/physrevx.8.041048 | Machine Learning A General-Purpose Interatomic Potential For Silicon | Bartok, Albert P.; Kermode, James; Bernstein, Noam; Csanyi, Gabor | Physical Review X | 2018 | NA |
| 10.1126/science.aau5631 | Prediction Of Higher-Selectivity Catalysts By Computer-Driven Workflow And Machine Learning | Zahrt, Andrew F., 0000-0002-1835-5163; Henle, Jeremy J., 0000-0001-9045-1726; Rose, Brennan T., 0000-0002-7225-3600; Wang, Yang, 0000-0002-7584-6188; Darrow, William T., 0000-0002-4784-0133; Denmark, Scott E., 0000-0002-1099-9765 | Science | 2019 | NA |
| 10.1126/science.aaw1147 | Boltzmann Generators: Sampling Equilibrium States Of Many-Body Systems With Deep Learning | Noe, Frank, 0000-0003-4169-9324; Olsson, Simon, 0000-0002-3927-7897; Kohler, Jonas, 0000-0002-7256-2892; Wu, Hao, 0000-0002-2170-0618 | Science | 2019 | NA |
| 10.1137/18m1177846 | Linearly Recurrent Autoencoder Networks For Learning Dynamics | Otto, Samuel E.; Rowley, Clarence W., 0000-0002-9099-5739 | Siam Journal On Applied Dynamical Systems | 2019 | NA |
| 10.1146/annurev-physchem-042018-052331 | Machine Learning For Molecular Simulation | Noe, Frank; Tkatchenko, Alexandre; Muller, Klaus-Robert; Clementi, Cecilia | Annual Review Of Physical Chemistry | 2020 | NA |